This code is for the caregiver speech measures from transcripts used in the manuscript.

Load libraries and set theme

library(tidyverse)
library(Hmisc)
library(GGally)
library(ppcor)
library(gridExtra)
library(psych)

# https://github.com/ggobi/ggally/issues/139
my_custom_smooth <- function(data, mapping, ...) {
  ggplot(data = data, mapping = mapping) +
    geom_point(alpha = .4, color = I("black")) + 
    geom_smooth(method = "lm", color = I("blue"), ...)
}

theme_set(theme_bw())

Read in data

# NOTE about periods of non-tCDCS
# gemods refers to when there are designated start/end periods of other-directed speech (ODS); this was captured using gems (@G) using CHAT conventions
# kwalods refers to when ODS was transcribed at an utterance-level within a tCDS activity period between caregiver and child (e.g., other-directed speech in the background); this was captured per utterances using CHAT postcodes
## for tokens/min and types/min, we do not include ODS that occurred within a period of tCDS, because durations were captured by activity and not by utterance
## for mlu, we include all ODS across gemods and kwalods


# NOTE about speech == "all"
# "speech" includes two levels: all, spont
# all = refers to all speech by caregivers
# spont = refers to only speech by caregivers that was considered spontaneous rather than recited (e.g., reading book text, singing memorized common songs like itsy bitsy spider); therefore, 'spont' is a subset of 'all'

# freq
freq <- read_csv("./data_demo_lena_transcripts/freq.csv") %>% 
  dplyr::select(-X1) %>% 
  filter(activity != "kwalods") %>% 
  filter(speech == "all") %>% 
  mutate(activity = factor(activity, levels = c("books", "play", "food", 
                                                "routines", "conv", "ac", 
                                                "gemods")), 
         id = factor(id), 
         language = factor(language), 
         speech = factor(speech))


# mlu
mlu <- read_csv("./data_demo_lena_transcripts/mlu.csv") %>% 
  mutate(activity = factor(activity, levels = c("books", "play", "food", 
                                                "routines", "conv", "ac", "ods")), 
         id = factor(id), 
         language = factor(language), 
         speech = factor(speech)) %>% 
  filter(speech == "all")


# chip
# this includes only caregivers, therefore there is no speaker column
# we exclude periods of ODS because this is about responsiveness to the child during periods of tCDS
chip <- read_csv("./data_demo_lena_transcripts/chip.csv") %>% 
  filter(activity != "ods") %>% 
  mutate(activity = factor(activity, levels = c("books", "play", "food", 
                                                "routines", "conv", "ac")), 
         id = factor(id), 
         language = factor(language)) 

str(freq)

## tibble[,12] [3,308 × 12] (S3: tbl_df/tbl/data.frame)
##  $ id           : Factor w/ 90 levels "7292","7352",..: 47 47 47 47 50 50 52 52 52 52 ...
##  $ rectime      : num [1:3308] 11923 11923 31360 31360 21499 ...
##  $ activity     : Factor w/ 7 levels "books","play",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ speaker      : chr [1:3308] "CHI" "ADULTS" "CHI" "ADULTS" ...
##  $ tokens       : num [1:3308] 30 151 35 143 58 588 42 286 33 152 ...
##  $ types        : num [1:3308] 17 70 17 65 17 199 19 53 17 59 ...
##  $ segment_num  : num [1:3308] 12 12 15 15 2 2 11 11 5 5 ...
##  $ language     : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
##  $ speech       : Factor w/ 1 level "all": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dur_min      : num [1:3308] 3.55 3.55 6.57 6.57 4.71 ...
##  $ tokens_permin: num [1:3308] 8.46 42.57 5.32 21.75 12.31 ...
##  $ types_permin : num [1:3308] 4.79 19.73 2.59 9.89 3.61 ...

str(mlu)

## spec_tbl_df[,9] [3,002 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ id         : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
##  $ activity   : Factor w/ 7 levels "books","play",..: 6 6 5 5 7 7 2 2 6 6 ...
##  $ speaker    : chr [1:3002] "ADULTS" "CHI" "ADULTS" "CHI" ...
##  $ segment_num: num [1:3002] 2 2 2 2 2 2 2 2 3 3 ...
##  $ words_sum  : num [1:3002] 210 66 175 43 11 16 189 47 261 78 ...
##  $ num_utt_sum: num [1:3002] 66 35 64 24 2 12 64 28 87 43 ...
##  $ mlu_w      : num [1:3002] 3.18 1.89 2.73 1.79 5.5 ...
##  $ language   : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
##  $ speech     : Factor w/ 2 levels "all","spont": 1 1 1 1 1 1 1 1 1 1 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   id = col_double(),
##   ..   activity = col_character(),
##   ..   speaker = col_character(),
##   ..   segment_num = col_double(),
##   ..   words_sum = col_double(),
##   ..   num_utt_sum = col_double(),
##   ..   mlu_w = col_double(),
##   ..   language = col_character(),
##   ..   speech = col_character()
##   .. )

str(chip)

## spec_tbl_df[,11] [1,118 × 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ activity                         : Factor w/ 6 levels "books","play",..: 6 5 2 6 5 4 6 5 4 2 ...
##  $ id                               : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
##  $ rectime                          : num [1:1118] 15242 15242 15242 14342 14342 ...
##  $ total_adult_utt                  : num [1:1118] 68 64 65 91 43 13 50 8 65 127 ...
##  $ total_child_utt                  : num [1:1118] 46 34 33 54 17 3 14 1 29 49 ...
##  $ total_adult_resp                 : num [1:1118] 62 51 54 77 24 9 30 4 56 106 ...
##  $ total_adult_imitexp              : num [1:1118] 18 13 15 25 5 2 9 0 16 21 ...
##  $ prop_adultresp_outof_childutt    : num [1:1118] 1.35 1.5 1.64 1.43 1.41 ...
##  $ prop_adult_imitexp_outof_childutt: num [1:1118] 0.391 0.382 0.455 0.463 0.294 ...
##  $ language                         : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
##  $ segment_num                      : num [1:1118] 2 2 2 3 3 3 4 4 4 5 ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   activity = col_character(),
##   ..   id = col_double(),
##   ..   rectime = col_double(),
##   ..   total_adult_utt = col_double(),
##   ..   total_child_utt = col_double(),
##   ..   total_adult_resp = col_double(),
##   ..   total_adult_imitexp = col_double(),
##   ..   prop_adultresp_outof_childutt = col_double(),
##   ..   prop_adult_imitexp_outof_childutt = col_double(),
##   ..   language = col_character(),
##   ..   segment_num = col_double()
##   .. )

Create dfs for ADULTS

# FREQ
freq_adult <- freq %>% 
  filter(speaker == "ADULTS")

# MLU
mlu_adult <- mlu %>% 
  filter(speaker == "ADULTS")

FREQ - Boxplots and descriptives for ADULTS

TOKENS (raw)

Freq (tokens, types)

excluded non-target children (NTC)
excluded overlapping ODS during CDS periods
averaged across all adult speakers
if an activity was not present, we did not include it (i.e., we did not impute any values to create complete cases)
0 values are included when the individual does not speak though we know they are present in the activity

# relabel for plots
freq_adult <- freq_adult %>% 
  mutate(activity = recode(activity, "conv" = "unst. conv.", "ac" = "adult-centered",
                                     "gemods" = "non_tcds"), 
         language = recode(language, "English" = "english", "Spanish" = "spanish"))


# plot for all speech
ggplot(freq_adult, aes(activity, tokens, fill = activity)) + 
  geom_boxplot() + 
  geom_jitter(alpha = .2) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) + 
  facet_wrap(~ language, ncol = 1) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "")

# summarize data per participant and activity (all speech only)
freq_adult_act <- freq_adult %>% 
  group_by(id, activity) %>% 
  mutate(tokens_act = mean(tokens), 
         types_act = mean(types)) %>% 
  distinct(id, language, activity, tokens_act, types_act)

freq_adult_act_en <- freq_adult_act %>% filter(language == "english")
freq_adult_act_sp <- freq_adult_act %>% filter(language == "spanish")



# descriptives
describeBy(freq_adult_act_en$tokens_act, freq_adult_act_en$activity, mat = T, fast = T)

##     item         group1 vars  n      mean        sd   min       max    range        se
## X11    1          books    1 22 480.90076 272.79828 147.0 1115.7500 968.7500 58.160789
## X12    2           play    1 39 234.99060 132.96267  42.5  714.0000 671.5000 21.291067
## X13    3           food    1 31 145.78495 124.42440  12.0  432.0000 420.0000 22.347282
## X14    4       routines    1 32 135.71823 107.06498   7.0  494.0000 487.0000 18.926592
## X15    5    unst. conv.    1 43 152.50504 100.82982  11.0  382.7500 371.7500 15.376403
## X16    6 adult-centered    1 45  90.56519  60.38539   4.0  295.0000 291.0000  9.001722
## X17    7       non_tcds    1 45 154.69407  96.87180  15.0  418.6667 403.6667 14.440795

freq_adult_en_minmax_tokens_raw <- freq_adult %>% 
  filter(language == "english") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(tokens, na.rm = T), 
         max = max(tokens, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_en_minmax_tokens_raw

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          english     65  1474
## 2 play           english      0   743
## 3 unst. conv.    english      4   817
## 4 routines       english      7   692
## 5 food           english      1   630
## 6 adult-centered english      0   397
## 7 non_tcds       english      0   945

describeBy(freq_adult_act_sp$tokens_act, freq_adult_act_sp$activity, mat = T, fast = T)

##     item         group1 vars  n     mean        sd       min      max    range       se
## X11    1          books    1 20 436.9875 212.49434 77.000000 768.0000 691.0000 47.51518
## X12    2           play    1 37 180.9775 136.02617 25.000000 571.0000 546.0000 22.36256
## X13    3           food    1 31 155.3602 119.25669  6.000000 445.0000 439.0000 21.41913
## X14    4       routines    1 35 166.9405 133.27275  4.000000 635.5000 631.5000 22.52721
## X15    5    unst. conv.    1 43 139.6674 123.38751 13.000000 754.6667 741.6667 18.81642
## X16    6 adult-centered    1 45 106.6911  89.50469 21.500000 426.5000 405.0000 13.34257
## X17    7       non_tcds    1 45 113.3085  82.01389  4.166667 323.5000 319.3333 12.22591

freq_adult_sp_minmax_tokens_raw <- freq_adult %>% 
  filter(language == "spanish") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(tokens, na.rm = T), 
         max = max(tokens, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_sp_minmax_tokens_raw

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          spanish     77   927
## 2 play           spanish     15   802
## 3 unst. conv.    spanish      5  1012
## 4 routines       spanish      4   937
## 5 food           spanish      6   566
## 6 adult-centered spanish      0   566
## 7 non_tcds       spanish      0   795

TOKENS (rate per min)

freq_adult <- freq_adult %>% 
  mutate(language = recode(language, "english" = "English", "spanish" = "Spanish"))


# plot for all speech
ggplot(freq_adult, aes(activity, tokens_permin, fill = activity)) + 
  theme_classic() +
  geom_boxplot() + 
  geom_jitter(alpha = .3) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) + 
  facet_wrap(~ language) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "Tokens Rate") + 
  theme(panel.spacing = unit(4, "lines"))

ggsave("./figures/boxplot_tokens_rate2.pdf", dpi = 300, width = 18, height = 8, units = "in")


# summarize data per participant and activity (all speech only)
freq_adult_act_permin <- freq_adult %>% 
  group_by(id, activity) %>% 
  mutate(tokens_permin_act = mean(tokens_permin), 
         types_permin_act = mean(types_permin)) %>% 
  distinct(id, language, activity, tokens_permin_act, types_permin_act)

freq_adult_act_permin_en <- freq_adult_act_permin %>% filter(language == "English")
freq_adult_act_permin_sp <- freq_adult_act_permin %>% filter(language == "Spanish")

# descriptives
describeBy(freq_adult_act_permin_en$tokens_permin_act, freq_adult_act_permin_en$activity, mat = T, fast = T)

##     item         group1 vars  n     mean       sd       min       max     range        se
## X11    1          books    1 22 97.05612 25.83716 32.157566 139.35186 107.19429  5.508500
## X12    2           play    1 39 63.00664 24.36993 19.809892 122.54285 102.73296  3.902312
## X13    3           food    1 31 63.20593 33.34061 17.818695 191.38756 173.56887  5.988150
## X14    4       routines    1 32 70.52775 27.24599 28.267219 157.18563 128.91841  4.816457
## X15    5    unst. conv.    1 43 76.14884 35.36817 10.266822 224.58716 214.32033  5.393596
## X16    6 adult-centered    1 45 85.01633 73.45787 19.605147 534.52116 514.91601 10.950453
## X17    7       non_tcds    1 45 36.98633 21.17975  4.063964  87.65849  83.59453  3.157290

freq_adult_en_minmax_tokens_rate <- freq_adult %>% 
  filter(language == "English") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(tokens_permin, na.rm = T), 
         max = max(tokens_permin, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_en_minmax_tokens_rate

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          English  21.7   149.
## 2 play           English   0     183.
## 3 unst. conv.    English   2.93  225.
## 4 routines       English  21.4   157.
## 5 food           English  12.3   191.
## 6 adult-centered English   0     535.
## 7 non_tcds       English   0     167.

describeBy(freq_adult_act_permin_sp$tokens_permin_act, freq_adult_act_permin_sp$activity, mat = T, fast = T)

##     item         group1 vars  n     mean       sd       min       max     range       se
## X11    1          books    1 20 69.70156 22.42204 33.415438 114.94578  81.53034 5.013720
## X12    2           play    1 37 51.27875 20.41805  8.535673 118.63334 110.09767 3.356707
## X13    3           food    1 31 37.39510 15.81841  9.649060  71.37418  61.72512 2.841070
## X14    4       routines    1 35 59.05829 22.87843 13.861479 118.88422 105.02275 3.867161
## X15    5    unst. conv.    1 43 59.56066 23.87057 24.971949 137.97635 113.00440 3.640229
## X16    6 adult-centered    1 45 56.17319 33.33514 14.119577 161.15476 147.03518 4.969310
## X17    7       non_tcds    1 45 31.05852 17.21041  2.157086  71.16063  69.00355 2.565577

freq_adult_sp_minmax_tokens_rate <- freq_adult %>% 
  filter(language == "Spanish") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(tokens_permin, na.rm = T), 
         max = max(tokens_permin, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_sp_minmax_tokens_rate

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          Spanish  33.4  115. 
## 2 play           Spanish   8.54 128. 
## 3 unst. conv.    Spanish  10.4  140. 
## 4 routines       Spanish  13.3  183. 
## 5 food           Spanish   9.65  91.1
## 6 adult-centered Spanish   0    343. 
## 7 non_tcds       Spanish   0    153.

TYPES (raw)

# df for just all speech
ggplot(freq_adult, aes(activity, types, fill = activity)) +
  geom_boxplot() + 
  geom_jitter(alpha = .2) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) + 
  facet_wrap(~ language) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "")

# descriptives
describeBy(freq_adult_act_en$types_act, freq_adult_act_en$activity, mat = T, fast = T)

##     item         group1 vars  n      mean       sd  min      max    range        se
## X11    1          books    1 22 143.90227 66.70063 56.0 277.7500 221.7500 14.220623
## X12    2           play    1 39  85.90256 36.92368 27.5 197.0000 169.5000  5.912521
## X13    3           food    1 31  64.56989 42.01631 12.0 177.0000 165.0000  7.546351
## X14    4       routines    1 32  58.62083 31.98695  7.0 144.0000 137.0000  5.654547
## X15    5    unst. conv.    1 43  65.25039 36.72048 10.0 163.0000 153.0000  5.599821
## X16    6 adult-centered    1 45  44.60630 24.26759  4.0 129.0000 125.0000  3.617598
## X17    7       non_tcds    1 45  79.91481 44.48101 10.5 199.8333 189.3333  6.630837

freq_adult_en_minmax_types_raw <- freq_adult %>% 
  filter(language == "English") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(types, na.rm = T), 
         max = max(types, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_en_minmax_types_raw

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          English     38   369
## 2 play           English      0   253
## 3 unst. conv.    English      4   267
## 4 routines       English      7   246
## 5 food           English      1   181
## 6 adult-centered English      0   159
## 7 non_tcds       English      0   351

describeBy(freq_adult_act_sp$types_act, freq_adult_act_sp$activity, mat = T, fast = T)

##     item         group1 vars  n      mean       sd  min      max    range        se
## X11    1          books    1 20 144.82500 64.74266 44.0 268.5000 224.5000 14.476900
## X12    2           play    1 37  62.33784 35.84100 14.0 157.0000 143.0000  5.892225
## X13    3           food    1 31  65.50538 36.39642  5.0 133.0000 128.0000  6.536989
## X14    4       routines    1 35  65.69762 33.60470  4.0 141.5000 137.5000  5.680232
## X15    5    unst. conv.    1 43  59.88527 32.98391  8.0 160.6667 152.6667  5.030000
## X16    6 adult-centered    1 45  48.42926 26.92731 15.0 121.0000 106.0000  4.014086
## X17    7       non_tcds    1 45  59.29000 36.12099  3.5 137.8333 134.3333  5.384600

freq_adult_sp_minmax_types_raw <- freq_adult %>% 
  filter(language == "Spanish") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(types, na.rm = T), 
         max = max(types, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_sp_minmax_types_raw

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          Spanish     44   361
## 2 play           Spanish      7   186
## 3 unst. conv.    Spanish      5   219
## 4 routines       Spanish      4   175
## 5 food           Spanish      5   166
## 6 adult-centered Spanish      0   212
## 7 non_tcds       Spanish      0   289

TYPES (rate per min)

# plot for all speech
ggplot(freq_adult, aes(activity, types_permin, fill = activity)) + 
  theme_classic() +
  geom_boxplot() + 
  geom_jitter(alpha = .3) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) + 
  facet_wrap(~ language) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "Types Rate")

ggsave("./figures/boxplot_types_rate2.pdf", dpi = 300, width = 18, height = 8, units = "in")


# descriptives
describeBy(freq_adult_act_permin_en$types_permin_act, freq_adult_act_permin_en$activity, mat = T, fast = T)

##     item         group1 vars  n     mean       sd       min       max     range        se
## X11    1          books    1 22 32.06383  9.75467 14.809274  48.75449  33.94522  2.079703
## X12    2           play    1 39 27.07891 12.32556 12.245120  66.31838  54.07326  1.973670
## X13    3           food    1 31 39.82161 34.99837  9.280570 191.38756 182.10699  6.285893
## X14    4       routines    1 32 41.75257 25.57367 14.409224 157.18563 142.77640  4.520830
## X15    5    unst. conv.    1 43 41.83628 27.40421  5.648173 176.14679 170.49862  4.179103
## X16    6 adult-centered    1 45 61.14224 75.86224 10.605640 534.52116 523.91552 11.308874
## X17    7       non_tcds    1 45 20.66782 11.41460  2.392211  48.90393  46.51172  1.701588

freq_adult_en_minmax_types_rate <- freq_adult %>% 
  filter(language == "English") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(types_permin, na.rm = T), 
         max = max(types_permin, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_en_minmax_types_rate

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          English   9.89  64.4
## 2 play           English   0    149. 
## 3 unst. conv.    English   2.93 176. 
## 4 routines       English  11.2  157. 
## 5 food           English   8.50 191. 
## 6 adult-centered English   0    535. 
## 7 non_tcds       English   0     92.2

describeBy(freq_adult_act_permin_sp$types_permin_act, freq_adult_act_permin_sp$activity, mat = T, fast = T)

##     item         group1 vars  n     mean        sd       min       max     range       se
## X11    1          books    1 20 25.19438  9.378178 10.866809  49.60044  38.73363 2.097024
## X12    2           play    1 37 21.98845 11.038304  4.405509  66.43467  62.02916 1.814686
## X13    3           food    1 31 19.09226  8.249124  8.576942  39.99831  31.42137 1.481586
## X14    4       routines    1 35 30.57141 14.693167  9.082726  73.61036  64.52764 2.483598
## X15    5    unst. conv.    1 43 32.41628 16.819376 10.061461 100.19711  90.13565 2.564931
## X16    6 adult-centered    1 45 38.00249 28.213513  7.901010 137.53863 129.63762 4.205822
## X17    7       non_tcds    1 45 18.23642  9.936926  1.666453  43.46532  41.79886 1.481309

freq_adult_sp_minmax_types_rate <- freq_adult %>% 
  filter(language == "spanish") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(types_permin, na.rm = T), 
         max = max(types_permin, na.rm = T)) %>% 
  distinct(activity, min, max)

freq_adult_sp_minmax_types_rate

## # A tibble: 0 x 4
## # Groups:   activity, language [0]
## # … with 4 variables: activity <fct>, language <fct>, min <dbl>, max <dbl>

MLU

excluded NTC
KEPT overlapping ODS during CDS periods
averaged across all adult speakers
if an activity was not present, we did not include it (i.e., we did not impute any values to create complete cases)
we excluded observations when there were 0 number of utterances, since we cannot calculate an MLU when there are no utterances

# relabel for plots
mlu_adult <- mlu_adult %>% 
  mutate(activity = recode(activity, "conv" = "unst. conv.", "ac" = "adult-centered",
                           "ods" = "non_tcds"), 
         language = recode(language, "english" = "English", "spanish" = "Spanish"))


# plot
ggplot(mlu_adult, aes(activity, mlu_w, fill = activity)) + 
  theme_classic() +
  geom_boxplot() + 
  geom_jitter(alpha = .3) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) + 
  facet_wrap(~ language) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "MLUw")

ggsave("./figures/boxplot_mluw2.pdf", dpi = 300, width = 18, height = 8, units = "in")


# summarize data per participant and activity (all speech only)
mlu_adult_act <- mlu_adult %>% 
  group_by(id, activity) %>% 
  mutate(mlu_w_act = mean(mlu_w)) %>% 
  distinct(id, language, activity, mlu_w_act)

mlu_adult_act_en <- mlu_adult_act %>% filter(language == "English")
mlu_adult_act_sp <- mlu_adult_act %>% filter(language == "Spanish")

# descriptives
describeBy(mlu_adult_act_en$mlu_w_act, mlu_adult_act_en$activity, mat = T, fast = T)

##     item         group1 vars  n     mean        sd      min      max    range        se
## X11    1          books    1 22 4.860163 1.2386474 2.843305 7.287671 4.444366 0.2640805
## X12    2           play    1 39 3.544910 0.7799806 2.117647 5.428571 3.310924 0.1248969
## X13    3           food    1 31 3.508170 0.8406000 2.125000 5.328869 3.203869 0.1509762
## X14    4       routines    1 32 3.665860 0.7357554 2.200000 5.670455 3.470455 0.1300644
## X15    5    unst. conv.    1 43 3.763843 0.8423445 1.500000 5.769231 4.269231 0.1284563
## X16    6 adult-centered    1 45 3.524902 0.7379635 2.248271 5.668605 3.420334 0.1100091
## X17    7       non_tcds    1 45 4.150646 0.8011976 2.542017 5.770249 3.228232 0.1194355

mlu_adult_en_minmax <- mlu_adult %>% 
  filter(language == "English") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(mlu_w, na.rm = T), 
         max = max(mlu_w, na.rm = T)) %>% 
  distinct(activity, min, max)

mlu_adult_en_minmax

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 adult-centered English   1     9   
## 2 unst. conv.    English   1     6.67
## 3 non_tcds       English   1     8   
## 4 play           English   1.33  5.69
## 5 routines       English   2.2   8   
## 6 books          English   1.51  8.73
## 7 food           English   1     6.06

describeBy(mlu_adult_act_sp$mlu_w_act, mlu_adult_act_sp$activity, mat = T, fast = T)

##     item         group1 vars  n     mean        sd      min      max    range         se
## X11    1          books    1 20 3.822411 1.3037314 1.864407 6.566855 4.702448 0.29152319
## X12    2           play    1 37 2.698639 0.7463847 1.422360 4.545918 3.123558 0.12270488
## X13    3           food    1 31 2.772200 0.6975939 1.391304 4.230769 2.839465 0.12529156
## X14    4       routines    1 35 3.029701 0.7381221 1.791667 4.485947 2.694281 0.12476540
## X15    5    unst. conv.    1 43 3.054863 0.5741442 2.004357 4.589286 2.584928 0.08755616
## X16    6 adult-centered    1 45 2.756766 0.6920525 1.666667 5.019476 3.352810 0.10316510
## X17    7       non_tcds    1 45 3.499380 0.7220236 1.890000 5.202889 3.312889 0.10763292

mlu_adult_sp_minmax <- mlu_adult %>% 
  filter(language == "Spanish") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(mlu_w, na.rm = T), 
         max = max(mlu_w, na.rm = T)) %>% 
  distinct(activity, min, max)

mlu_adult_sp_minmax

## # A tibble: 7 x 4
## # Groups:   activity, language [7]
##   activity       language   min   max
##   <fct>          <fct>    <dbl> <dbl>
## 1 books          Spanish   1.86  8.36
## 2 play           Spanish   1.21  6.48
## 3 unst. conv.    Spanish   1     5.33
## 4 routines       Spanish   1.58  5   
## 5 food           Spanish   1.39  4.32
## 6 adult-centered Spanish   1     6.22
## 7 non_tcds       Spanish   1     7.9

CHIP

Responses (RAW and PROP)

CHIP (responses, imitations/expansions; these are utterances that follow a child’s utterance, within a 5 utterance window)

excluded NTC
excluded overlapping ODS during CDS periods
averaged across all adult speakers
if an activity was not present, we did not include it (i.e., we did not impute any values to create complete cases)
we keep 0 values, which are because the adults did not respond even though the child did have utterances [e.g., numerator is 0 but denominator is a value]; if a child did not have utterances, then adult responses could not be calculated so the number of observations differ here from freq and mlu
greater than 1 = caregiver had more utterances in response to the child; less than 1 = child had more utterances than caregiver responses

# create dfs
chip2 <- chip %>% 
  mutate(activity = recode(activity, "conv" = "unst. conv.", "ac" = "adult-centered"), 
         language = recode(language, "english" = "English", "spanish" = "Spanish"))


# plot - total adult responses
ggplot(chip2, aes(activity, total_adult_resp, fill = activity)) + 
  geom_boxplot() + 
  geom_jitter(alpha = .2) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) + 
  facet_wrap(~ language, ncol = 1) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "")

# plot - prop of adult resp/child utt
ggplot(chip2, aes(activity, prop_adultresp_outof_childutt, fill = activity)) + 
  theme_classic() +
  geom_boxplot() + 
  geom_jitter(alpha = .3) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) + 
  facet_wrap(~ language) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  geom_hline(yintercept = 1) + # caregiver and child = equal utts
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "Prop Responses")

ggsave("./figures/boxplot_prop_resp2.pdf", dpi = 300, width = 18, height = 8, units = "in")


# summarize data per participant and activity (all speech only)
chip_act <- chip %>% 
  group_by(id, activity) %>% 
  mutate(prop_resp_act = mean(prop_adultresp_outof_childutt), 
         prop_imitexp_act = mean(prop_adult_imitexp_outof_childutt)) %>% 
  distinct(id, language, activity, prop_resp_act, prop_imitexp_act)

chip_act_en <- chip_act %>% filter(language == "english")
chip_act_sp <- chip_act %>% filter(language == "spanish")


# descriptives
describeBy(chip_act_en$prop_resp_act, chip_act_en$activity, mat = T, fast = T)

##     item   group1 vars  n     mean        sd       min      max    range         se
## X11    1    books    1 22 2.378070 0.7766933 1.1864443 4.366667 3.180222 0.16559157
## X12    2     play    1 36 2.074418 0.6357208 1.1301724 4.666667 3.536494 0.10595346
## X13    3     food    1 29 1.914779 0.5461100 0.8141026 3.066667 2.252564 0.10141009
## X14    4 routines    1 28 1.856904 0.5897762 0.8000000 3.333333 2.533333 0.11145723
## X15    5     conv    1 36 1.866984 0.5430045 0.6250000 2.933333 2.308333 0.09050074
## X16    6       ac    1 22 1.855095 0.5401576 0.9909502 3.248677 2.257727 0.11516198

chip_adult_en_minmax_propresp <- chip %>% 
  filter(language == "english") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(prop_adultresp_outof_childutt, na.rm = T), 
         max = max(prop_adultresp_outof_childutt, na.rm = T)) %>% 
  distinct(activity, min, max)

chip_adult_en_minmax_propresp

## # A tibble: 6 x 4
## # Groups:   activity, language [6]
##   activity language   min   max
##   <fct>    <fct>    <dbl> <dbl>
## 1 ac       english  0      5   
## 2 conv     english  0.571  5   
## 3 play     english  0.667  4.67
## 4 routines english  0.5    3.8 
## 5 books    english  0.859  5   
## 6 food     english  0      5

describeBy(chip_act_sp$prop_resp_act, chip_act_sp$activity, mat = T, fast = T)

##     item   group1 vars  n     mean        sd       min      max    range         se
## X11    1    books    1 20 2.125135 0.4832411 1.0617284 3.055556 1.993827 0.10805599
## X12    2     play    1 31 1.927563 0.6742332 0.8125000 3.500000 2.687500 0.12109586
## X13    3     food    1 30 1.762135 0.7100370 0.3333333 3.314815 2.981481 0.12963442
## X14    4 routines    1 31 1.904239 0.5335071 1.0000000 3.166667 2.166667 0.09582070
## X15    5     conv    1 35 1.818201 0.5605011 0.8823529 3.119048 2.236695 0.09474197
## X16    6       ac    1 25 1.589711 0.6019761 0.7083333 3.750000 3.041667 0.12039523

chip_adult_sp_minmax_propresp <- chip %>% 
  filter(language == "spanish") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(prop_adultresp_outof_childutt, na.rm = T), 
         max = max(prop_adultresp_outof_childutt, na.rm = T)) %>% 
  distinct(activity, min, max)

chip_adult_sp_minmax_propresp

## # A tibble: 6 x 4
## # Groups:   activity, language [6]
##   activity language   min   max
##   <fct>    <fct>    <dbl> <dbl>
## 1 ac       spanish  0      5   
## 2 conv     spanish  0.7    5   
## 3 play     spanish  0.765  5   
## 4 food     spanish  0.333  4   
## 5 routines spanish  1      5   
## 6 books    spanish  1.06   3.06

Imitations/Expansions (RAW and PROP)

# plot
ggplot(chip2, aes(activity, total_adult_imitexp, fill = activity)) + 
  geom_boxplot() + 
  geom_jitter(alpha = .2) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) + 
  facet_wrap(~ language, ncol = 1) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "")

# plot
ggplot(chip2, aes(activity, prop_adult_imitexp_outof_childutt, fill = activity)) + 
  theme_classic() + 
  geom_boxplot() + 
  geom_jitter(alpha = .3) + 
  scale_fill_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) + 
  facet_wrap(~ language) + 
  theme(legend.position= "none") +
  theme(text = element_text(size = 35)) +
  geom_hline(yintercept = 1) + # caregiver and child = equal utts
  theme(axis.text.x = element_text(angle = 20, hjust = .7)) + 
  labs(x = "", y = "Prop Imitations/Expansions")

ggsave("./figures/boxplot_prop_imitexp2.pdf", dpi = 300, width = 18, height = 8, units = "in")


# descriptives
describeBy(chip_act_en$prop_imitexp_act, chip_act_en$activity, mat = T, fast = T)

##     item   group1 vars  n      mean        sd        min       max     range         se
## X11    1    books    1 22 0.4822402 0.2032150 0.07142857 0.8000000 0.7285714 0.04332558
## X12    2     play    1 36 0.4082746 0.2249434 0.00000000 1.1157407 1.1157407 0.03749056
## X13    3     food    1 29 0.3716799 0.1958653 0.00000000 1.0000000 1.0000000 0.03637128
## X14    4 routines    1 28 0.3527558 0.1764344 0.00000000 0.6746032 0.6746032 0.03334296
## X15    5     conv    1 36 0.3490170 0.1925299 0.00000000 0.8888889 0.8888889 0.03208831
## X16    6       ac    1 22 0.3648964 0.1674221 0.18972991 0.8666667 0.6769368 0.03569452

chip_adult_en_minmax_propimitexp <- chip %>% 
  filter(language == "english") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(prop_adult_imitexp_outof_childutt, na.rm = T), 
         max = max(prop_adult_imitexp_outof_childutt, na.rm = T)) %>% 
  distinct(activity, min, max)

chip_adult_en_minmax_propimitexp

## # A tibble: 6 x 4
## # Groups:   activity, language [6]
##   activity language    min   max
##   <fct>    <fct>     <dbl> <dbl>
## 1 ac       english  0       4   
## 2 conv     english  0       2   
## 3 play     english  0       1.38
## 4 routines english  0       1   
## 5 books    english  0.0714  1   
## 6 food     english  0       1

describeBy(chip_act_sp$prop_imitexp_act, chip_act_sp$activity, mat = T, fast = T)

##     item   group1 vars  n      mean        sd    min       max     range         se
## X11    1    books    1 20 0.4052845 0.2414756 0.0625 1.0000000 0.9375000 0.05399559
## X12    2     play    1 31 0.3536352 0.2224772 0.0000 1.1700000 1.1700000 0.03995808
## X13    3     food    1 30 0.3333968 0.2401772 0.0000 1.0000000 1.0000000 0.04385016
## X14    4 routines    1 31 0.3949755 0.2920670 0.0000 1.0322636 1.0322636 0.05245678
## X15    5     conv    1 35 0.3856342 0.2314362 0.0000 0.9523810 0.9523810 0.03911986
## X16    6       ac    1 25 0.3015461 0.1634934 0.0000 0.7603067 0.7603067 0.03269868

chip_adult_sp_minmax_propimitexp <- chip %>% 
  filter(language == "spanish") %>% 
  group_by(activity, language) %>% 
  mutate(min = min(prop_adult_imitexp_outof_childutt, na.rm = T), 
         max = max(prop_adult_imitexp_outof_childutt, na.rm = T)) %>% 
  distinct(activity, min, max)

chip_adult_sp_minmax_propimitexp

## # A tibble: 6 x 4
## # Groups:   activity, language [6]
##   activity language   min   max
##   <fct>    <fct>    <dbl> <dbl>
## 1 ac       spanish      0  1.67
## 2 conv     spanish      0  2   
## 3 play     spanish      0  2   
## 4 food     spanish      0  1   
## 5 routines spanish      0  3   
## 6 books    spanish      0  1

Descriptives of Caregiver Speech - Transcription data

Janet Bang

November 17, 2022